Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the last few years and now employs modern variants of deep neural networks to extract relevant patterns from large text corpora. The main objective of this work is to survey the recent use of NLP in the field of pharmacology. As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology. It has been used extensively, from intelligent searches through thousands of medical documents to finding traces of adversarial drug interactions in social media. We split our coverage into five categories to survey modern NLP methodology, commonly addressed tasks, relevant textual data, knowledge bases, and useful programming libraries. We split each of the five categories into appropriate subcategories, describe their main properties and ideas, and summarize them in a tabular form. The resulting survey presents a comprehensive overview of the area, useful to practitioners and interested observers.

New York Attorney General Letitia James is asking Madison Square Garden Entertainment Corp. for information related to its alleged use of facial recognition technology to prevent certain ticket holders from entering its venues. The state attorney general's office said Wednesday the company, which operates Madison Square Garden and Radio City Music Hall in New York City, has reportedly used the technology to bar lawyers from firms who are suing the company over unrelated matters from seeing sporting events or concerts.

DARPA (Defense Advanced Research Projects Agency) is developing a new system to help military personnel perform complex tasks. Perceptually-enabled Task Guidance (PTG) technology uses sensors to see and hear what the user sees and hears, guiding them through AI-produced instructions displayed in augmented reality (AR). PTG combines sensors (a microphone and head-mounted camera) with AI and AR headsets to integrate into the user's environment. The idea is to help soldiers and other military personnel enhance their skills, complete complicated tasks and perform them better. DARPA has narrowed its focus to three areas: battlefield medicine (like untrained personnel helping medics in the field), sustainment (keeping military equipment up and running) and co-piloting (especially helicopters).

New York Attorney General Letitia James has sent a letter to MSG Entertainment, the owner and operator of Madison Square Garden and Radio City Music Hall, asking for information about its use of facial recognition to deny entry to attorneys at firms representing its legal opponents. James's letter warns that the Orwellian policy may violate local, state and federal human rights laws, including those prohibiting retaliation. MSG Entertainment's facial recognition has been identifying and denying entry to lawyers from firms representing clients suing the company -- whether or not those attorneys are directly involved in the cases. The company, led by CEO James Dolan (who also owns the New York Knicks and Rangers), has defended the policy, framing it as an attempt to prevent evidence collection "outside proper litigation discovery channels." However, lawyers have called that rationale "ludicrous," criticizing the ban as a "transparent effort" to punish attorneys for suing them.

Rep. Brian Fitzpatrick, R-Penn., and Rep. Josh Gottheimer, D-N.J., discuss the latest news emerging from the classified documents seized from President Biden on'Fox News Sunday.' This is a rush transcript of'Fox News Sunday' from January 22nd, 2022. This copy may not be in its final form and may be updated. A new round of classified items found in the president's home and new concerns about financial fallouts as the U.S. hits the debt limit again. JIM CLYBURN (D-SC): We've had these games before and it should not be done. KARINE JEAN-PIERRE, WHITE HOUSE PRESS SECRETARY: The president has been clear on this. It should not be used as a political weapon. BREAM: Swing district, moderate Republicans are calling for the president to drop the take it or leave it approach and come to the table. We'll sit down for a bipartisan conversation with two co-chairs from the Problem Solvers Caucus. Republican Brian Fitzpatrick and Democrat Josh Gottheimer join me to talk about how to find consensus on the debt limit, immigration and more. Then -- thousands of pro-life advocates come to the nation's capital for the first March for Life since the Supreme Court overturned Roe v. Wade. We'll look at the legal state of play now that abortion laws are up to the states, and sit down for a conversation with prominent voices from both sides. And eight months after the unprecedented leak of a draft Supreme Court ruling, there are still no answers from the high court about the leaker. JIM JORDAN (R-OH): The only way you're going to stop this in the future is to make sure you find out who did it and hold them accountable. BREAM: We'll ask our Sunday panel if we will ever find out who did it. Breaking overnight, at least ten people are dead, another ten injured after a mass shooting near Los Angeles. It happened late last night at a dance club in Monterey Park, California, close to where a lunar New York celebration had been taking place. Authorities say they believe the shooter is male and at this time it appears that person is not in custody. Deputies say they are reviewing security video in that area. Monterey Park is about ten miles east of Los Angeles. We'll keep you updated on any developments we get in from there. Also breaking this morning, the Justice Department seized more classified documents from the president's private residence just this week. The news comes as President Biden prepares to speak in person with House Speaker Kevin McCarthy to discuss the new Congress, a range of challenges there, where they disagree. And that, of course, includes the debt limit. Congress is facing a deadline to strike a deal or risk a financial crisis as the Treasury department steps in to avoid a government default.

Background checks and ID verification systems in dating apps are among the measures being considered as governments around the country grapple with how to keep people safe while they are looking for love online. The strategies were discussed by ministers, victim-survivors, authorities and technology companies as part of national dating app roundtable talks in Sydney on Wednesday. The federal communications minister, Michelle Rowland, said it was an "important first step", flagging discussion of possible longer-term changes like background checks for dating app users. "None of us underestimate the complex issues around privacy, user safety, data collection and management that are involved," she said. "There's no one law that is going to fix this issue."

Gross domestic product (GDP) is the most widely used indicator in macroeconomics and the main tool for measuring a country's economic ouput. Due to the diversity and complexity of the world economy, a wide range of models have been used, but there are challenges in making decadal GDP forecasts given unexpected changes such as pandemics and wars. Deep learning models are well suited for modeling temporal sequences have been applied for time series forecasting. In this paper, we develop a deep learning framework to forecast the GDP growth rate of the world economy over a decade. We use Penn World Table as the source of our data, taking data from 1980 to 2019, across 13 countries, such as Australia, China, India, the United States and so on. We test multiple deep learning models, LSTM, BD-LSTM, ED-LSTM and CNN, and compared their results with the traditional time series model (ARIMA,VAR). Our results indicate that ED-LSTM is the best performing model. We present a recursive deep learning framework to predict the GDP growth rate in the next ten years. We predict that most countries will experience economic growth slowdown, stagnation or even recession within five years; only China, France and India are predicted to experience stable, or increasing, GDP growth.

Influence function, a method from robust statistics, measures the changes of model parameters or some functions about model parameters concerning the removal or modification of training instances. It is an efficient and useful post-hoc method for studying the interpretability of machine learning models without the need for expensive model re-training. Recently, graph convolution networks (GCNs), which operate on graph data, have attracted a great deal of attention. However, there is no preceding research on the influence functions of GCNs to shed light on the effects of removing training nodes/edges from an input graph. Since the nodes/edges in a graph are interdependent in GCNs, it is challenging to derive influence functions for GCNs. To fill this gap, we started with the simple graph convolution (SGC) model that operates on an attributed graph and formulated an influence function to approximate the changes in model parameters when a node or an edge is removed from an attributed graph. Moreover, we theoretically analyzed the error bound of the estimated influence of removing an edge. We experimentally validated the accuracy and effectiveness of our influence estimation function. In addition, we showed that the influence function of an SGC model could be used to estimate the impact of removing training nodes/edges on the test performance of the SGC without re-training the model. Finally, we demonstrated how to use influence functions to guide the adversarial attacks on GCNs effectively.

Uncertainty in perception, actuation, and the environment often require multiple attempts for a robotic task to be successful. We study a class of problems providing (1) low-entropy indicators of terminal success / failure, and (2) unreliable (high-entropy) data to predict the final outcome of an ongoing task. Examples include a robot trying to connect with a charging station, parallel parking, or assembling a tightly-fitting part. The ability to restart after predicting failure early, versus simply running to failure, can significantly decrease the makespan, that is, the total time to completion, with the drawback of potentially short-cutting an otherwise successful operation. Assuming task running times to be Poisson distributed, and using a Markov Jump process to capture the dynamics of the underlying Markov Decision Process, we derive a closed form solution that predicts makespan based on the confusion matrix of the failure predictor. This allows the robot to learn failure prediction in a production environment, and only adopt a preemptive policy when it actually saves time. We demonstrate this approach using a robotic peg-in-hole assembly problem using a real robotic system. Failures are predicted by a dilated convolutional network based on force-torque data, showing an average makespan reduction from 101s to 81s (N=120, p<0.05). We posit that the proposed algorithm generalizes to any robotic behavior with an unambiguous terminal reward, with wide ranging applications on how robots can learn and improve their behaviors in the wild.

Knowledge-intensive tasks, such as open-domain question answering (QA), require access to a large amount of world or domain knowledge. A common approach for knowledge-intensive tasks is to employ a retrieve-then-read pipeline that first retrieves a handful of relevant contextual documents from an external corpus such as Wikipedia and then predicts an answer conditioned on the retrieved documents. In this paper, we present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer. Furthermore, we propose a novel clustering-based prompting method that selects distinct prompts, resulting in the generated documents that cover different perspectives, leading to better recall over acceptable answers. We conduct extensive experiments on three different knowledge-intensive tasks, including open-domain QA, fact checking, and dialogue system. Notably, GenRead achieves 71.6 and 54.4 exact match scores on TriviaQA and WebQ, significantly outperforming the state-of-the-art retrieve-then-read pipeline DPR-FiD by +4.0 and +3.9, without retrieving any documents from any external knowledge source. Lastly, we demonstrate the model performance can be further improved by combining retrieval and generation. Our code and generated documents can be found at https://github.com/wyu97/GenRead.